| Summary: | The aim of this thesis was to study and test how inexpensive temperature loggers can be used to measure the local and microscale variability of the snowmelt processes and rates in subarctic Pallastunturi fell area. The loggers were deployed on six test plots with varied topography, vegetation and terrain type. In each test plot the sensors were installed in five test points on the ground and above the ground on fixed height of 30 cm. During the installation, the snowpack height and density were measured from each test point. The temperature was recorded at 15 min interval from 19th of April to 15th of June 2014. The melting processes and rates were determined using diurnal temperature fluctuations of the sensors. Validity of the results was evaluated using snow height data from adjacent measurement stations maintained by Geological Survey of Finland, equipped with acoustic snow sensor. Additionally an empirical snow model was employed to test the determined melt rates using climate data from Finnish Meteorological Institute as input for the model. The results exposed the difference in timing and variability of the snowmelt. Timing was earlier in southern slopes and slightly earlier in open areas compared forests. The variability of the melt timing was highest at forested plots whereas it was lowest at an open mire. The results agreed reasonably well with the measurement results from the acoustic measurement station but gave also information about the spatial variability of the melt. Determined melt rates were used in an empirical degree-day snow model to estimate the snow water equivalent between 1st of September 2013 to 31st of August 2014. The root mean squared error between the model and measured dates for the end of permanent snow cover for the data from 24 test points was 3.74 days. The microscale accuracy of the method was highest in relatively homogenous and open terrain type, such as open mire, where the accuracy was approximately one day. In more complex terrain types with forests the method was less ...
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